Titlebook: Hybrid Intelligent Systems; Larry R. Medsker Book 1995 Kluwer Academic Publishers 1995 adaptive control.algorithms.case-based reasoning.co

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Summary and the Future of Hybrid Intelligent Systems,rk has been done on conventional hardware, but future work will include implementation with massively parallel systems, optical computing, and other hardware as they are improved and become economical. Hybrid intelligent systems (see Figure 14.1) also rely on and interface with conventional software

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Research in Hybrid Intelligent Systems,t the various aspects of human intelligence that are necessary for enhancing decision making in computing systems. In addition to the practical aspect, systems that perform a variety of intelligent tasks are interesting also for expanding the artificial intelligence effort to gain a better understanding of human cognition.

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Expert Systems and Neural Networks,ome cases, neural computing systems are replacing expert systems and other artificial intelligence solutions. In other applications, neural networks provide features not possible with conventional AI systems and may provide aspects of intelligent behavior that have thus far eluded AI.

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Expert Networks: Theory and Applications, major thrust of this type of hybrid system is to synthesize the capability of expert systems to capture expert domain knowledge in an inference-based system with the power of black-box neural networks trained from example data.

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Genetic Algorithms and Neural Networks,ndividually. In the early 1990’s, the revolution in the research and application of neural networks was followed by a surge in activity for genetic algorithms. By 1994, several conferences and publications had been dedicated to genetic algorithms where they had formerly been an adjunct of neural network or expert systems forums.